Recommendations for the integration of genomics into clinical practice
Bibliographic record
Abstract
The introduction of diagnostic clinical genome and exome sequencing (CGES) is changing the scope of practice for clinical geneticists. Many large institutions are making a significant investment in infrastructure and technology, allowing clinicians to access CGES, especially as health-care coverage begins to extend to clinically indicated genomic sequencing-based tests. Translating and realizing the comprehensive clinical benefits of genomic medicine remain a key challenge for the current and future care of patients. With the increasing application of CGES, it is necessary for geneticists and other health-care providers to understand its benefits and limitations in order to interpret the clinical relevance of genomic variants identified in the context of health and disease. New, collaborative working relationships with specialists across diverse disciplines (e.g., clinicians, laboratorians, bioinformaticians) will undoubtedly be key attributes of the future practice of clinical genetics and may serve as an example for other specialties in medicine. These new skills and relationships will also inform the development of the future model of clinical genetics training curricula. To address the evolving role of the clinical geneticist in the rapidly changing climate of genomic medicine, two Clinical Genetics Think Tank meetings were held that brought together physicians, laboratorians, scientists, genetic counselors, trainees, and patients with experience in clinical genetics, genetic diagnostics, and genetics education. This article provides recommendations that will guide the integration of genomics into clinical practice.Genet Med 18 11, 1075-1084.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".